This paper proposed an adaptive explicit nonlinear model predictive control (AENMPC) technique using multiple estimation models with a convex combination framework [18] for a class of nonlinear MIMO systems. Here, the explicit solution for the control signal is obtained from an optimal performance index which can be formulated without online optimization. In this work, a closed-form control law is developed by approximating the tracking error in the receding horizon by its Taylor series expansion. The control performance of any model-based control technologies explicitly depends on the quality of the unknown system parameters hence an adaptive parameter estimator is used to estimate the system parameter online [16,17]. To ensure the boundedness of the estimated parameter within a predefined compact region, a projection based adaptive law is used [43]. Using an aerodynamic laboratory set-up, known as the twin-rotor multi-input multi-output system (TRMS), the effectiveness of the proposed control algorithm has been verified. The complete state information of the system to the proposed adaptive controller is given from an extended Kalman filter based state observer. The performance of the proposed adaptive control algorithm has been verified successfully in simulations as well as real-time experimental setup of the TRMS model and compared with an existing control approach.

本文提出了一种自适应的显式非线性模型预测控制（AENMPC）技术，该技术使用具有凸组合框架[18]的多个估计模型来处理一类非线性MIMO系统。在此，可以从最佳性能指标中获得控制信号的显式解决方案，而无需在线优化就可以制定出最佳性能指标。在这项工作中，通过其泰勒级数展开来近似后退层中的跟踪误差，从而开发出一种封闭形式的控制定律。任何基于模型的控制技术的控制性能都明显取决于未知系统参数的质量，因此使用自适应参数估计器在线估计系统参数[16,17]。为了确保估计的参数在预定义的紧凑区域内的有界性，使用基于投影的自适应定律[43]。使用一种称为双转子多输入多输出系统（TRMS）的空气动力学实验室设置，已验证了所提出的控制算法的有效性。由扩展的基于卡尔曼滤波器的状态观测器给出了系统对建议的自适应控制器的完整状态信息。所提出的自适应控制算法的性能已在TRMS模型的仿真以及实时实验设置中得到了成功验证，并与现有的控制方法进行了比较。由扩展的基于卡尔曼滤波器的状态观测器给出了系统对建议的自适应控制器的完整状态信息。所提出的自适应控制算法的性能已在TRMS模型的仿真以及实时实验设置中得到了成功验证，并与现有的控制方法进行了比较。由扩展的基于卡尔曼滤波器的状态观测器给出了系统对建议的自适应控制器的完整状态信息。所提出的自适应控制算法的性能已在TRMS模型的仿真以及实时实验设置中得到了成功验证，并与现有的控制方法进行了比较。